NotGrandma'sServer
This is not Grandma’s exchange server
A
medical researcher studying the structure and interaction of proteins
found the resulting diagrams resembled a huge black hairball. IBM came
to the rescue with a machine that does 12 teraflops
By: Kathleen Lau
ComputerWorld Canada
(27 Jun 2008)
The Ontario Cancer Institute has
deployed what it’s calling Canada’s fastest
research supercomputer to help discover more effective cancer
treatments.
The
goal of the research, which has been under way for 10 years is to
understand cancer by developing and applying various algorithms in
order to analyze large quantities of complex data. Besides the Ontario
Cancer Institute, the project team consists of scientists from Princess
Margaret Hospital, University Health Network and Buffalo’s
Hauptman-Woodward Medical Research Institute.
The
project lead scientist, Igor
Jurisica,
said that designing new cancer drugs requires analyses of protein
interactions that when displayed on a screen is much like “a
huge black
hairball.” Different algorithms must then be applied to help
interpret
the mass of data.
Another
part of the research is understanding the structure of proteins
in order to devise cancer treatments. This requires the creation of
crystals, and therefore determining optimal conditions to creating
quality crystals. But the approach begets a “massive
information
technology problem,” said Jurisica, with the combination of
proteins
and conditions resulting in more than 90 million images to analyze and
interpret.
“So
our task is to have algorithms that look through all those images and
classify them to find the results of the experiment. That’s
where we
need this massive computing power to be able to handle this
complexity,” said Jurisica.
The
technology is the IBM System Cluster 1350 supercomputer that
incorporates the DCS9550 Disk Storage System, as well as Deep Computing
Visualization to create high-resolution images required for the
research analysis. The system also includes 1,344 processor cores in
the Linux cluster running at 12.5 teraflops (trillion calculations per
second) with 150 TB.
The
deployment was made possible by grants from the Canada Foundation for
Innovation and the Ontario Ministry of Research and Innovation. IBM
provided in-kind donation for the hardware, software and services.
Chris
Pratt, strategy initiatives executive with the Armonk, New York-based
company sees the long-standing relationship with the research team as a
collaborative one, where IBM’s role goes beyond merely
providing the IT
infrastructure. “It’s the conceptual design and
scoping of the problem
to provisioning and supplying of equipment, and making sure it works
and does what it’s supposed to do,” said Pratt.
“This is not grandma’s exchange server,”
he said, “This is a very complex type of problem that
requires specific skills.”
And,
as of last November, the project was running its 90 million analyses on
the World Community Grid’s network of 250,000 PCs.
“Even with that
power, we will finish in 2014,” said Jurisica, adding that
data
garnered from the World Community Grid would require massive computing
power to run analyses.
Computations
that were taking months on the old infrastructure now takes days.
“So
it’s really an order of magnitude change how quickly we can
run these
analyses,” said Jurisica.
IBM’s
interest in research projects reflects its belief that innovation in
one domain is applicable to others. “Across research, we
think of it as
being very specifically focused, but very often discoveries in one area
of research lead to leaps forward in other areas of research that were
not related,” said Pratt.
Image analysis, said Pratt, may have initially been solely associated
with photography, not cancer research.
During
the project’s infant days, Jurisica recalled the
crystallization
process was performed manually by humans using glass pipettes. But
supercomputing has changed the picture with an injection of speed and
accuracy.
“Without
information technology, there is no way to interpret these results
because machines are spitting out so much data every second, you must
have enough capacity to store it and analyze and interpret the
results.”
“Biology
can’t really move forward without automation,” he
said.
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